Ambiguity-driven fuzzy C-means clustering: how to detect uncertain clustered records

As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input sample to belong to more than one cluster, providing more flexibility than non-fuzzy clustering methods. However, the accuracy of FCM is subject to false detections caused by noisy records, weak feature selection and low certainty of the algorithm in some cases. The false detections are very important in some decision-making application domains like network security and medical diagnosis, where weak decisions based on such false detections may lead to catastrophic outcomes. They mainly emerge from making decisions about a subset of records that do not provide sufficient evidence to make a good decision. In this paper, we propose a method for detecting such ambiguous records in FCM by introducing a certainty factor to decrease invalid detections. This approach enables us to send the detected ambiguous records to another discrimination method for a deeper investigation, thus increasing the accuracy by lowering the error rate. Most of the records are still processed quickly and with low error rate preventing performance loss which is common in similar hybrid methods. Experimental results of applying the proposed method on several datasets from different domains show a significant decrease in error rate as well as improved sensitivity of the algorithm.     

Design and analysis of DC gain and transconductance boosted recycling folded cascode OTA

A new technique for improving the transconductance and low frequency output impedance of recycling folded cascode (RFC) amplifiers is presented. This enhancement was achieved by using a positive feedback and upgrading the recycling structure. The new structure profits from better transconductance, slew rate, and DC gain in comparison with conventional folded cascode (FC) amplifier. Moreover, the input referred noise is reduced and the phase-margin improved. The enhanced amplifier, simulated in 0.18 μm CMOS technology, exhibits a DC gain enhancement of 16.3 dB as well as 115.5 MHz increase in gain bandwidth compared to conventional FC configuration. The amplifier consumes 360 μW @ 1.2 V which makes it suitable for low-voltage applications.     

The effect of mass irregularities of weft yarns on fabric surface protrusion

Fabric surface unevenness and defects are usually created by yarn irregularity and defects in weaving process. These unevenness and irregularities appear on fabric and affect various fabric properties. In order to investigate weft yarn mass irregularities on fabric surface unevenness and defects, various plain fabric samples were prepared in which they differ only in weft yarn mass irregularity. One of the effective factors on fabric surface unevenness is yarn protrusion in fabric structure. Yarn protrusion in fabric structure is influenced by yarn’s physical and mechanical properties and fabric structural characteristics. In this work, relationship between mass irregularities of weft yarn and fabric surface unevenness was investigated using angular power spectrum curve, a measure of yarn protrusion in fabric structures. The results showed a high significant correlation between these two parameters.     

A comparative study on some properties of wood plastic composites using canola stalk,Paulownia, and nanoclay

In this research, the reinforcing effect of fillers including canola stalk, paulownia and nanoclay, in polypropylene (PP) has been investigated. In the sample preparation, 50 wt% of lignocellulosic materials and 0, 3, and 5 wt% of nanoclay particles were used. The results showed that while flexural and tensile properties were moderately enhanced by the addition of nanoclay in the matrix, notched Izod impact strengths decreased dramatically. However, with increase in the nanoclay content (5 wt%), the flexural and tensile properties decreased considerably. The mechanical properties of composites filled with paulownia are generally greater than canola stalk composites, due to the higher aspect ratio. The thickness swelling and water absorption of the composites significantly decreased with the increase in nanoclay loading. Except tensile modulus, the differences between the type of fibrous materials and nanoclay contents had significant influence on physicomechanical properties. Morphologies of the composites were analyzed using transmission electron microscopy (TEM) and X‐ray diffraction (XRD), and the results showed increased d‐spacing of clay layers indicating enhanced compatibility among PP, clay, and lignocellulosic material. TEM micrographs also confirmed that the composites containing 3 wt% nanoclay had uniform dispersion and distribution of clay layers in the polymer matrix. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Cloud service selection using cloud service brokers: approaches and challenges

Cloud computing users are faced with a wide variety of services to choose from. Consequently, a number of cloud service brokers (CSBs) have emerged to help users in their service selection process. This paper reviews the recent approaches that have been introduced and used for cloud service brokerage and discusses their challenges accordingly. We propose a set of attributes for a CSB to be considered effective. DifFerent CSBs' approaches are classified as either single service or multiple service models. The CSBs are then assessed, analyzed, and compared with respect to the proposed set of attributes. Based on our studies, CSBs with multiple service models that support more of the proposed effective CSB attributes have wider application in cloud computing environments.     

Impact of a large penetration of wind generation on the GB gas network

Wind power is expected to be the major element of renewable electricity generation in Great Britain (GB) by 2020 with a capacity of around 30 GW. The potential impact of a large amount of wind generation on the GB gas network was investigated using a combined gas and electricity network model. The varying nature of gas and electric power flows, network support facilities such as gas storage and compressors, and the power ramping characteristics of various power plants were considered. Three case studies were modelled, one case uses the existing network and the other two make use of a hypothesised network in 2020 with two distinct levels of wind generation representing low and high wind periods. The simulation results show that a large penetration of wind generation will influence the electricity generation mix as the wind power varies. Gas-fired generation is used to compensate for wind variability. This will cause increased flows and compressor power consumption on the gas network. Linepack depletion during low wind periods was shown to limit the ability of the gas network to fully supply gas-fired generators.     

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme learning machines, including the standard, online sequential, and kernel extreme learning machines, in addition to the artificial neural network, classification and regression tree model, and statistical multiple linear regression model. The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models. The input variability was assessed based on two scenarios prior to the application of the predictive models. For the first assessment, the machine learning models were developed using all the available cement and concrete mixture input variables; the second assessment was conducted based on the gamma test approach, which is a sensitivity analysis technique. Subsequently, the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches. The adopted methodology attained optimistic and reliable modeling results. The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.     

A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects

Time estimation in new product development (NPD) projects is often a complex problem due to its nonlinearity and the small quantity of data patterns. Support vector regression (SVR) based on statistical learning theory is introduced as a new neural network technique with maximum generalization ability. The SVR has been utilized to solve nonlinear regression problems successfully. However, the applicability of the SVR is highly affected due to the difficulty of selecting the SVR parameters appropriately. The imperialist competitive algorithm (ICA) as a socio-politically inspired optimization strategy is employed to solve the real world engineering problems. This optimization algorithm is inspired by competition mechanism among imperialists and colonies, in contrast to evolutionary algorithms. This paper presents a new model integrating the SVR and the ICA for time estimation in NPD projects, in which ICA is used to tune the parameters of the SVR. A real data set from a case study of an NPD project in a manufacturing industry is presented to demonstrate the performance of the proposed model. In addition, the comparison is provided between the proposed model and conventional techniques, namely nonlinear regression, back-propagation neural networks (BPNN), pure SVR and general regression neural networks (GRNN). The experimental results indicate that the presented model achieves high estimation accuracy and leads to effective prediction.     

Effect of Initial Microstructure on Impact Toughness of 1200 MPa-Class High Strength Steel with Ultrafine Elongated Grain Structure

A medium-carbon low-alloy steel was prepared with initial structures of either martensite or bainite. For both initial structures, warm caliber-rolling was conducted at 773 K (500 °C) to obtain ultrafine elongated grain (UFEG) structures with strong 〈110〉//rolling direction (RD) fiber deformation textures. The UFEG structures consisted of spheroidal cementite particles distributed uniformly in a ferrite matrix of a transverse grain size of about 331 and 311 nm in samples with initial martensite and bainite structures, respectively. For both initial structures, the UFEG materials had similar tensile properties, upper shelf energy (145 J), and ductile-to-brittle transition temperatures 98 K (500 °C). Obtaining the martensitic structure requires more rapid cooling than is needed to obtain the bainitic structure and this more rapid cooling promote cracking. As the UFEG structures obtained from initial martensitic and bainitic structures have almost identical properties, but obtaining the bainitic structure does not require a rapid cooling which promotes cracking suggests the use of a bainitic structure in obtaining UFEG structures should be examined further.     

Fault-tolerant control of linear systems using adaptive virtual actuator

In this paper, design and development of fault-tolerant control (FTC) is investigated for linear systems subject to loss of effectiveness and time-varying additive actuator faults as well as an external disturbance using the fault-hiding approach. The main aim of this approach is to keep the nominal controller and to design a virtual actuator that is inserted between the faulty plant and the nominal controller in order to hide actuator faults and disturbances from the nominal controller, and consequently the performance of the system before and after the occurrence of actuator faults is kept to be the same. The proposed adaptive virtual actuator does not require a separated fault detection, isolation and identification (FDII) unit and both state and output feedback cases are considered. An illustrative example is given to demonstrate the effectiveness of the proposed adaptive virtual actuator in both cases.